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Molecular networks in Network Medicine: Development and applications
WIREs Mechanisms of Disease ( IF 3.1 ) Pub Date : 2020-04-19 , DOI: 10.1002/wsbm.1489 Edwin K. Silverman 1 , Harald H. H. W. Schmidt 2 , Eleni Anastasiadou 3 , Lucia Altucci 4 , Marco Angelini 5 , Lina Badimon 6 , Jean‐Luc Balligand 7 , Giuditta Benincasa 8 , Giovambattista Capasso 9, 10 , Federica Conte 11 , Antonella Di Costanzo 4 , Lorenzo Farina 5 , Giulia Fiscon 11 , Laurent Gatto 12, 13 , Michele Gentili 5 , Joseph Loscalzo 1, 14 , Cinzia Marchese 3 , Claudio Napoli 8 , Paola Paci 5 , Manuela Petti 5 , John Quackenbush 1, 15 , Paolo Tieri 16 , Davide Viggiano 10, 17 , Gemma Vilahur 6 , Kimberly Glass 1, 15 , Jan Baumbach 18, 19
WIREs Mechanisms of Disease ( IF 3.1 ) Pub Date : 2020-04-19 , DOI: 10.1002/wsbm.1489 Edwin K. Silverman 1 , Harald H. H. W. Schmidt 2 , Eleni Anastasiadou 3 , Lucia Altucci 4 , Marco Angelini 5 , Lina Badimon 6 , Jean‐Luc Balligand 7 , Giuditta Benincasa 8 , Giovambattista Capasso 9, 10 , Federica Conte 11 , Antonella Di Costanzo 4 , Lorenzo Farina 5 , Giulia Fiscon 11 , Laurent Gatto 12, 13 , Michele Gentili 5 , Joseph Loscalzo 1, 14 , Cinzia Marchese 3 , Claudio Napoli 8 , Paola Paci 5 , Manuela Petti 5 , John Quackenbush 1, 15 , Paolo Tieri 16 , Davide Viggiano 10, 17 , Gemma Vilahur 6 , Kimberly Glass 1, 15 , Jan Baumbach 18, 19
Affiliation
Network Medicine applies network science approaches to investigate disease pathogenesis. Many different analytical methods have been used to infer relevant molecular networks, including protein–protein interaction networks, correlation‐based networks, gene regulatory networks, and Bayesian networks. Network Medicine applies these integrated approaches to Omics Big Data (including genetics, epigenetics, transcriptomics, metabolomics, and proteomics) using computational biology tools and, thereby, has the potential to provide improvements in the diagnosis, prognosis, and treatment of complex diseases. We discuss briefly the types of molecular data that are used in molecular network analyses, survey the analytical methods for inferring molecular networks, and review efforts to validate and visualize molecular networks. Successful applications of molecular network analysis have been reported in pulmonary arterial hypertension, coronary heart disease, diabetes mellitus, chronic lung diseases, and drug development. Important knowledge gaps in Network Medicine include incompleteness of the molecular interactome, challenges in identifying key genes within genetic association regions, and limited applications to human diseases.
中文翻译:
网络医学中的分子网络:开发与应用
网络医学应用网络科学方法来研究疾病的发病机理。许多不同的分析方法已用于推断相关的分子网络,包括蛋白质-蛋白质相互作用网络,基于相关的网络,基因调控网络和贝叶斯网络。Network Medicine使用计算生物学工具将这些集成方法应用于Omics大数据(包括遗传学,表观遗传学,转录组学,代谢组学和蛋白质组学),因此具有改善复杂疾病的诊断,预后和治疗的潜力。我们简要讨论了分子网络分析中使用的分子数据的类型,调查了推断分子网络的分析方法,并回顾了验证和可视化分子网络的工作。已经报道了分子网络分析在肺动脉高压,冠心病,糖尿病,慢性肺病和药物开发中的成功应用。网络医学中的重要知识缺口包括分子相互作用组的不完整,在遗传关联区域内鉴定关键基因的挑战以及对人类疾病的有限应用。
更新日期:2020-04-19
中文翻译:
网络医学中的分子网络:开发与应用
网络医学应用网络科学方法来研究疾病的发病机理。许多不同的分析方法已用于推断相关的分子网络,包括蛋白质-蛋白质相互作用网络,基于相关的网络,基因调控网络和贝叶斯网络。Network Medicine使用计算生物学工具将这些集成方法应用于Omics大数据(包括遗传学,表观遗传学,转录组学,代谢组学和蛋白质组学),因此具有改善复杂疾病的诊断,预后和治疗的潜力。我们简要讨论了分子网络分析中使用的分子数据的类型,调查了推断分子网络的分析方法,并回顾了验证和可视化分子网络的工作。已经报道了分子网络分析在肺动脉高压,冠心病,糖尿病,慢性肺病和药物开发中的成功应用。网络医学中的重要知识缺口包括分子相互作用组的不完整,在遗传关联区域内鉴定关键基因的挑战以及对人类疾病的有限应用。